Meteorological Parameter Forecasting

ABSTRACT

A method, an apparatus and an article of manufacture for forecasting a meteorological parameter. The method includes analyzing geographically distributed sensor network data to assess spatial and temporal variation of a meteorological parameter in real-time, correlating at least two portions of data from the sensor network to identify a temporal and spatial evolution of the meteorological parameter, and forecasting the meteorological parameter based on the temporal and spatial evolution of the meteorological parameter.

FIELD OF THE INVENTION

Embodiments of the invention generally relate to information technology,and, more particularly, to meteorological technology.

BACKGROUND OF THE INVENTION

Weather forecasting, cloud movement prediction and gaseouscontaminations distribution from a localized source are most commonlyacquired using satellite data. In existing approaches, the spatialresolutions of the acquired data are of the orders of tens of miles anddata localization on a map grid can be shifted from the realgeographical locations. Complex models commonly take the high spatialresolution satellite data and combine it with first principle modelingto achieve a local prediction. Based on physical models the weather,solar radiation or contamination is estimated and predicted locally andprojected over the long term.

Satellite based models may work on the time scales of hours up to daysbut would be highly inaccurate for prediction on short term time scalesuch as minutes. In such cases, local measurements and predictive modelsare developed for attempted accuracy of short term predictions. Multipleforecasting methods have to be employed to extend the prediction fromseconds up to days. The forecasts rely on different information and thedegree of physical information that is embedded in such a model changesfrom one method to another. Forecasting at a timescale, from seconds upto days in advance, the long term (satellite) and short term (localsensors) measurement and prediction has to be combined. To bridge thegap between different data sets and establish a smooth transition fromprediction based on different data sets requires physical models toestablish the coupling parameters between the two observational models.

Distributed sensor networks are commonly encountered today on largescale geographical areas, such as, for example, solar panels mountednearby roads and highways systems used for monitoring traffic, solarpanels on the roofs of houses that are distributed over largegeographical areas, air quality measurement by government agencies thatmonitor sets of parameters in many cities across the world, satellitebased observations or mobile sensor networks such as sensors that areintegrated in cars or cell phones such as light sensors for turning onheadlights at dusk or locations based on global positioning system (GPS)signals.

Local sensors that can be employed for short term forecasting include,for example, sky cameras to observe cloud movement, pyronometers tomeasure solar radiation, and corrosion sensors to predict the gaseouscontamination of the atmosphere.

SUMMARY OF THE INVENTION

In one aspect of the present invention, techniques for meteorologicalparameter forecasting are provided. An exemplary computer-implementedmethod for forecasting a meteorological parameter can include steps ofanalyzing geographically distributed sensor network data to assessspatial and temporal variation of a meteorological parameter inreal-time, correlating at least two portions of data from the sensornetwork to identify a temporal and spatial evolution of themeteorological parameter, and forecasting the meteorological parameterbased on the temporal and spatial evolution of the meteorologicalparameter.

Another aspect of the invention or elements thereof can be implementedin the form of an article of manufacture tangibly embodying computerreadable instructions which, when implemented, cause a computer to carryout a plurality of method steps, as described herein. Furthermore,another aspect of the invention or elements thereof can be implementedin the form of an apparatus including a memory and at least oneprocessor that is coupled to the memory and operative to perform notedmethod steps. Yet further, another aspect of the invention or elementsthereof can be implemented in the form of means for carrying out themethod steps described herein, or elements thereof; the means caninclude (i) hardware module(s), (ii) software module(s), or (iii) acombination of hardware and software modules; any of (i)-(iii) implementthe specific techniques set forth herein, and the software modules arestored in a tangible computer-readable storage medium (or multiple suchmedia).

These and other objects, features and advantages of the presentinvention will become apparent from the following detailed descriptionof illustrative embodiments thereof, which is to be read in connectionwith the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an example sensor network map,according to an embodiment of the present invention;

FIG. 2 is a diagram illustrating example neighbor sensor data, accordingto an embodiment of the present invention;

FIG. 3 is a diagram illustrating an example divided set of data,according to an embodiment of the present invention;

FIG. 4 is a diagram illustrating an example subsequence of data sets,according to an embodiment of the present invention;

FIG. 5 is a block diagram illustrating an example embodiment, accordingto an aspect of the invention;

FIG. 6 is a flow diagram illustrating techniques for forecastingphysical parameters, according to an embodiment of the invention;

FIG. 7 is a flow diagram illustrating techniques for forecasting ameteorological parameter, according to an embodiment of the invention;and

FIG. 8 is a system diagram of an exemplary computer system on which atleast one embodiment of the invention can be implemented.

DETAILED DESCRIPTION OF EMBODIMENTS

As described herein, an aspect of the present invention includes usingdistributed sensor network (for example, an integrated wide arealocal/global ground-based sensor network) for forecasting solarpower/intensity, cloud movement, and atmospheric gaseous contamination.Geographically distributed sensor network data are used to assess thetemporal variation of atmospheric parameters such as solar radiation,wind, cloud movement or atmospheric gaseous contamination. The denselypositioned sensors are distributed over a large geographical area andmeasurements are performed in real-time.

The data obtained from the near neighbors is correlated with data fromsensors farther positioned to obtain patterns that are shifted in time(for example, clouds moving from one location to the next location wouldbe observed as a time correlated event in the power production of solarpanels that are distributed across a geographical area and are along thepath that cloud is traveling). In an aspect of the invention, real-timeresponses of sensors are used to predict physical parameters in theproximity of a measurement based on long term tracking and analysis ofclouds movement and/or wind direction. Additionally, data fromclosely-spaced sensors can be used to forecast events on a short-timescale while extending the area of sensors analyzed can be used toforecast on a larger and larger time scale. The larger time scale canalso be used in connection with geographical distribution of the events.

The forecasting can be calibrated using other global sensors such assatellites that track, on a very large scale, the atmosphere, gaseouscontaminations (SO₂, NO₂, ozone, dust particles, etc.) and/or solarradiation. For short and long term forecasting, an aspect of theinvention includes using global observation of physical parameters(satellite measurements) integrated with local measurements to tightenthe parameter space used for forecasting. The time evolution of weatherparameters such as, for example, change in form and propagationdirection of a cloud, can be analyzed and predicted based onmeasurements from a large set of ground sensors (for example, skycameras, pyronometers, light detectors, etc.) to predict evolution andinduced response at nearby geographically locations.

An aspect of the invention includes providing a system that uses solarpanel power output to measure cloud distribution, cloud movementdirection and speed. The drop in solar power due to clouds covering thesun will be directly dependent on the cloud optical properties. Powerprovided is directly related to radiation, and this information is usedto predict cloud movement in the sky, as well as to forecast cloudmovement. Solar panels used in conjunction with an aspect of theinvention provide a higher spatial resolution and a higher temporalresolution than current satellite imaging system that has a very coarseresolution.

The techniques and system detailed herein can also be used to forecastpower production from distributed solar farms. Based on a time intervalbetween correlated events extracted from neighbor sensors, the cloudspeeds can be estimated. Placing the solar farm at distances wherecorrelation is lost or minimized will ensure that power drops on nearbysolar farms will be uncorrelated and the power production can besmoothened over a large distance.

To determine the clouds speed, every power drop data point has a timestamp associated therewith. When the first sensor detects an event, thetime stamp is stored in a memory. When the second sensor detects ahighly correlated event, the new time stamp is recorded. Knowing thedistance between the two sensors, the speed of the traveling cloud isdetermined as the distance divided by the difference in time between theevents recorded by the two sensors. For directional prediction, the dataset is divided into regions to facilitate identification of patterns ofevents.

FIG. 1 is a diagram illustrating an example sensor network map,according to an embodiment of the present invention. By way ofillustration, FIG. 1 depicts an example sensor network map 102,containing a network of sensor nodes 104. Accordingly, in a preferredembodiment of the invention, data from a distributed sensor network isanalyzed for spatial and temporal variations to extract short and longterm correlation. The data from a set of sensors can be used to train aphysical model for realistic prediction. The basis of cloud detection orsolar power production relies on physical models. These models mustinclude the physical properties of the studied object, such as theoptical properties of the clouds that would create a certain solarradiation model on the ground. Such optical properties may be altered byother factors such as humidity, contaminants existing in the atmosphere,etc. To correlate the effect detected by the distributed sensor network,a model can take into account these types of local variations. Once theeffect of local parameters is integrated into models, the correlationmay be more prevalent. The outcome of the model can be verified againstmeasurements that are performed continuously.

By way of example, a set of sensors distributed over an area can be usedto detect cloud movement, wind speed and direction, temperature, solarradiations and/or gaseous contaminations based on correlating themeasurement data. It should be appreciated that other parameters can beused in the present invention as well. The sensors are distributed overa large spatial area, as illustrated in FIG. 1, and they record inreal-time the physical parameters of interest. The data is analyzed inreal-time to extract reoccurring patterns and correlations, startingfrom the nearest neighbors and extending to farther positionedneighbors.

Additionally, the data can be analyzed for two distinct variations: thespatial and the short and long term temporal variations. The data fromthe nearest neighbors' sensors are correlated, and then the analysis isextended to the second order neighbors. The sensors data from the tworegions are analyzed for correlations and pattern occurrence due totemporal variations. In an aspect of the invention, this same processiteratively continues with farther and farther positioned neighbors aslong as the correlation exists.

FIG. 2 is a diagram illustrating example neighbor sensor data, accordingto an embodiment of the present invention. By way of illustration, FIG.2 depicts sensor nodes (for example, 202), nearest neighbors 204 andsecond order neighbors 206. The sensor data from the nearest neighborsgives a snapshot of the physical parameters of interest at every momentof time. By analyzing the data from neighbor sensors (the sensorslocated inside the first circle in FIG. 2) and extracting time-basedcorrelations, the evolution of the physical parameters can be extracted.Also, based on time intervals between correlated events extracted fromneighbor sensors, the speed of propagation can be estimated. Fordirectional prediction, the data set is divided into regions.

FIG. 3 is a diagram illustrating an example divided set of data,according to an embodiment of the present invention. By way ofillustration, FIG. 3 depicts region 1 (302), region 2 (304), region 3(306), region 4 (308), region 5 (310) and region 6 (312). For example,as illustrated in FIG. 3, events occurring in region 1 are correlatedwith events that are occurring in regions 2, 3, 4, 5, 6, etc. Forexample, time series analysis of sampled data are analyzed to extract afiner directional information and the extent to which the original eventas detected in region 1 is extending at a later moment in region 2, 3,4, etc. Because the sensors have fixed position, detecting how clouds orcontamination is propagating includes assessing the strength ofcorrelation for sensors in adjacent regions. Also, because the timestamp of the event is known, the strength of correlation providesinformation as to how the event is extending and also as to the maindirection of propagation as determined by external factors. The regionthat has the highest correlations and similar pattern will provide thedirection of propagation. The strength of correlation and time when thecorrelation occur will determine the degree of correlation or lack ofcorrelation.

Similarly the data can be analyzed as a different subsequence of datasets, such as depicted in FIG. 4.

FIG. 4 is a diagram illustrating an example subsequence of data sets,according to an embodiment of the present invention. By way ofillustration, FIG. 4 depicts first order neighbor data 402, second orderneighbor data 404 and third order neighbor data 406. Additionally, thefirst, second and third order neighbor data can be analyzed to extractthe direction and speed of propagation of events. Once the speed ofpropagation and direction is known, physical models can be used topredict events that are appearing at sensors located in the next blocksneighboring the analyzed region. For instance, one example is todetermine how contamination is propagating away from a point source andto determine the factors that determine its geographical extent and howconcentrations of the pollutants decrease away from the source ofpollution. There are multiple factors that contribute to thecontamination propagation, such as strength and direction of wind, howquickly the contamination is getting mixed-in with the air and howdilution is occurring over a large area, etc.

The model of data analysis can be used to predict, on a short-termbasis, the propagation of a noted event from a sensor to the nextneighbor sensors. Also, in an aspect of the invention, the prediction isverified based on sensor measurements that are tracking the event thatwas forecasted based on the model in certain geographical locations.Specifically, the prediction is verified by continuously monitoring theevent in question in the nearby neighbors to ensure that the speed anddirection determinations are not suddenly/unexpectedly changing.Further, the model can be self-adjusted to take into account both theprediction and the real measurement data to improve the forecasting.

As described herein, data from the distributed sensor network canprovide a snapshot of physical parameters in a given moment of time. Forexample, the power output of solar panels positioned near roads andhighways would give a snapshot of the cloud distribution over the entiregeographical area that is covered by the ground based sensors. The localmeasurements can be used also to recalibrate the larger data setsobtained from satellite and used for weather predictions. Also, thedistributed sensor nodes can be used to forecast events and used forenergy forecasting such as wind, solar or air contamination propagation.

Accordingly, as detailed herein, an aspect of the invention includesusing and analyzing distributed sensor network data is proposed both forspatial and for temporal variations of weather, solar radiation and/oratmospheric contaminations. The real-time data from the sensor networkis cross-correlated and analyzed for pattern occurrence to identify andtrack the time evolution of physical parameters of interest. Theparameters are used as inputs in physical models to predict and toforecast both in space and in time. Additionally, in an aspect of theinvention, the model is continuously recalibrated based on data from thesensors that are located at and around the area where the prediction wasmade. The distributed sensor network gives a snapshot of the physicalparameters over a very large geographical area, providing an effectivemethod to understand, for example, weather parameters and how theyevolve over time.

FIG. 5 is a block diagram illustrating an example embodiment, accordingto an aspect of the invention. By way of illustration, FIG. 5 depicts adistributed sensor network 502, a storage component 504 and a computercomponent 506. According to an aspect of the invention, the distributedsensor network 502 carries out sensor data collection via module 508,and the storage component 504 stores data in module 510. Further, thecomputer component 506 carries out a Fast Fourier Transform (FFT)process in module 512 and determines if a correlation exists via module514. Additionally, if no correlation exists, module 518 chooses anotherset of sensors and revisiting storage module 510. If a correlation doesexist, module 516 extends the time interval and revisiting storagemodule 510.

FIG. 6 is a flow diagram illustrating techniques for forecastingphysical parameters, according to an embodiment of the presentinvention. Step 602 includes setting a time interval dt. Step 604includes reading a first sensor for dt and obtaining a Fast FourierTransform (FFT). Step 606 includes reading adjacent sensors and FFTs.Additionally, step 608 includes correlating data from the first sensorand the other (adjacent) sensors.

Step 610 includes determining if (portions of) that data is correlated.If there are no correlations, step 612 includes choosing another sensorand the process returns to step 604. If there is a correlation, step 614includes determining the strength of the correlation. Further, step 616includes determining the speed and direction of the correlation and step622 includes extending the correlation to the next neighbor sensors.Additionally, step 618 includes including the correlation in a physicalmodel, and step 620 includes forecasting physical parameters.

FIG. 7 is a flow diagram illustrating techniques for forecasting ameteorological parameter, according to an embodiment of the presentinvention. Step 702 includes analyzing geographically distributed sensornetwork data to assess spatial and temporal variation of ameteorological parameter in real-time. This step can be carried out, forexample, using a module as described herein. By way of example, ameteorological parameter can include weather, wind, cloud movement,solar radiation, atmospheric gaseous contamination, etc. Additionallyanalyzing geographically distributed sensor network data includesextracting at least one pattern. Also, the sensor data can be obtained,for example, from a wired network such as the internet, power linecommunications, or wireless network, where the sensors are in closeproximity and data is sent from one node to another. Further, by way ofexample, sensor data can be obtained through satellite communicationssuch as cell phone message texting, where data is sent through thatnetwork.

Step 704 includes correlating at least two portions of data (forexample, data sets) from the sensor network to identify a temporal andspatial evolution of the meteorological parameter. This step can becarried out, for example, using a module as described herein.Correlating at least two portions of data includes correlating dataobtained from a near neighbor sensor with data obtained from at leastone farther-positioned sensor to determine patterns that are shifted intime.

Step 706 includes forecasting the meteorological parameter based on thetemporal and spatial evolution of the meteorological parameter. Thisstep can be carried out, for example, using a module as describedherein. Forecasting the meteorological parameter based on the temporaland spatial evolution of the meteorological parameter includes traininga physical model for a meteorological parameter prediction based on datafrom at least one sensor. The data from at least one sensor can include,for example, determined speed of propagation and direction ofpropagation for the meteorological parameter. Additionally, speed ofpropagation of the meteorological parameter is estimated based on a timeinterval between at least two correlated events extracted fromneighboring sensors, and direction of propagation of the meteorologicalparameter is determined via dividing the sensor network into regions tofacilitate identification of patterns of events.

Also, an aspect of the invention includes predicting an event at asensor located in a block neighboring an analyzed region based on thephysical model. Further, the prediction can be verified based oncontinuing sensor measurements that track the events. Additionally, themodel can be self-adjusted to take into account the prediction andreal-time measurement data to improve forecasting. An aspect of theinvention can also includes continuously recalibrating the model basedon data from sensors located at and around an area neighboring ananalyzed region.

The techniques depicted in FIG. 7 can also, as described herein, includeproviding a system, wherein the system includes distinct softwaremodules, each of the distinct software modules being embodied on atangible computer-readable recordable storage medium. All the modules(or any subset thereof) can be on the same medium, or each can be on adifferent medium, for example. The modules can include any or all of thecomponents shown in the figures. In an aspect of the invention, themodules can run, for example on a hardware processor. The method stepscan then be carried out using the distinct software modules of thesystem, as described above, executing on the hardware processor.Further, a computer program product can include a tangiblecomputer-readable recordable storage medium with code adapted to beexecuted to carry out at least one method step described herein,including the provision of the system with the distinct softwaremodules.

Additionally, the techniques depicted in FIG. 7 can be implemented via acomputer program product that can include computer useable program codethat is stored in a computer readable storage medium in a dataprocessing system, and wherein the computer useable program code wasdownloaded over a network from a remote data processing system. Also, inan aspect of the invention, the computer program product can includecomputer useable program code that is stored in a computer readablestorage medium in a server data processing system, and wherein thecomputer useable program code are downloaded over a network to a remotedata processing system for use in a computer readable storage mediumwith the remote system.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in a computer readable medium havingcomputer readable program code embodied thereon.

An aspect of the invention, or elements thereof, can be implemented inthe form of an apparatus including a memory and at least one processorthat is coupled to the memory and operative to perform exemplary methodsteps.

Additionally, an aspect of the present invention can make use ofsoftware running on a general purpose computer or workstation. Withreference to FIG. 8, such an implementation might employ, for example, aprocessor 802, a memory 804, and an input/output interface formed, forexample, by a display 806 and a keyboard 808. The term “processor” asused herein is intended to include any processing device, such as, forexample, one that includes a CPU (central processing unit) and/or otherforms of processing circuitry. Further, the term “processor” may referto more than one individual processor. The term “memory” is intended toinclude memory associated with a processor or CPU, such as, for example,RAM (random access memory), ROM (read only memory), a fixed memorydevice (for example, hard drive), a removable memory device (forexample, diskette), a flash memory and the like. In addition, the phrase“input/output interface” as used herein, is intended to include, forexample, a mechanism for inputting data to the processing unit (forexample, mouse), and a mechanism for providing results associated withthe processing unit (for example, printer). The processor 802, memory804, and input/output interface such as display 806 and keyboard 808 canbe interconnected, for example, via bus 810 as part of a data processingunit 812. Suitable interconnections, for example via bus 810, can alsobe provided to a network interface 814, such as a network card, whichcan be provided to interface with a computer network, and to a mediainterface 816, such as a diskette or CD-ROM drive, which can be providedto interface with media 818.

Accordingly, computer software including instructions or code forperforming the methodologies of the invention, as described herein, maybe stored in at least one of the associated memory devices (for example,ROM, fixed or removable memory) and, when ready to be utilized, loadedin part or in whole (for example, into RAM) and implemented by a CPU.Such software could include, but is not limited to, firmware, residentsoftware, microcode, and the like.

A data processing system suitable for storing and/or executing programcode will include at least one processor 802 coupled directly orindirectly to memory elements 804 through a system bus 810. The memoryelements can include local memory employed during actual implementationof the program code, bulk storage, and cache memories which providetemporary storage of at least some program code in order to reduce thenumber of times code must be retrieved from bulk storage duringimplementation.

Input/output or I/O devices (including but not limited to keyboards 808,displays 806, pointing devices, and the like) can be coupled to thesystem either directly (such as via bus 810) or through intervening I/Ocontrollers (omitted for clarity).

Network adapters such as network interface 814 may also be coupled tothe system to enable the data processing system to become coupled toother data processing systems or remote printers or storage devicesthrough intervening private or public networks. Modems, cable modem andEthernet cards are just a few of the currently available types ofnetwork adapters.

As used herein, including the claims, a “server” includes a physicaldata processing system (for example, system 812 as shown in FIG. 8)running a server program. It will be understood that such a physicalserver may or may not include a display and keyboard.

As noted, aspects of the present invention may take the form of acomputer program product embodied in a computer readable medium havingcomputer readable program code embodied thereon. Also, any combinationof computer readable mediums may be utilized. The computer readablemedium may be a computer readable storage medium such as, for example,but not limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, or device, or any suitablecombination of the foregoing. Media block 818 is a non-limiting example.More specific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving at least one wire, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing an appropriate medium or any suitable combination of multiplemedia.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of at least oneprogramming language, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks. Accordingly, an aspect of the inventionincludes an article of manufacture tangibly embodying computer readableinstructions which, when implemented, cause a computer to carry out aplurality of method steps as described herein.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, component, segment,or portion of code, which comprises at least one executable instructionfor implementing the specified logical function(s). It should also benoted that, in some alternative implementations, the functions noted inthe block may occur out of the order noted in the figures. For example,two blocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It should be noted that any of the methods described herein can includean additional step of providing a system comprising distinct softwaremodules embodied on a computer readable storage medium; the modules caninclude, for example, any or all of the components shown in FIG. 5. Themethod steps can then be carried out using the distinct software modulesand/or sub-modules of the system, as described above, executing on ahardware processor 802. Further, a computer program product can includea computer-readable storage medium with code adapted to be implementedto carry out at least one method step described herein, including theprovision of the system with the distinct software modules.

In any case, it should be understood that the components illustratedherein may be implemented in various forms of hardware, software, orcombinations thereof; for example, application specific integratedcircuit(s) (ASICS), functional circuitry, an appropriately programmedgeneral purpose digital computer with associated memory, and the like.Given the teachings of the invention provided herein, one of ordinaryskill in the related art will be able to contemplate otherimplementations of the components of the invention.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a,” “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition ofanother feature, integer, step, operation, element, component, and/orgroup thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

At least one aspect of the present invention may provide a beneficialeffect such as, for example, using solar panel power output to measurecloud distribution and type.

It will be appreciated and should be understood that the exemplaryembodiments of the invention described above can be implemented in anumber of different fashions. Given the teachings of the inventionprovided herein, one of ordinary skill in the related art will be ableto contemplate other implementations of the invention. Indeed, althoughillustrative embodiments of the present invention have been describedherein with reference to the accompanying drawings, it is to beunderstood that the invention is not limited to those preciseembodiments, and that various other changes and modifications may bemade by one skilled in the art.

1-13. (canceled)
 14. An article of manufacture tangibly embodying computer readable instructions which, when implemented, cause a computer to carry out a plurality of method steps comprising: analyzing geographically distributed sensor network data to assess spatial and temporal variation of a meteorological parameter in real-time; correlating at least two portions of data from the sensor network to identify a temporal and spatial evolution of the meteorological parameter; and forecasting the meteorological parameter based on the temporal and spatial evolution of the meteorological parameter.
 15. The article of manufacture of claim 14, wherein correlating at least two portions of data comprises correlating data obtained from a near neighbor sensor with data obtained from at least one farther-positioned sensor to determine a pattern that is shifted in time.
 16. The article of manufacture of claim 14, wherein forecasting the meteorological parameter based on the temporal and spatial evolution of the meteorological parameter comprises training a physical model for a meteorological parameter prediction based on data from at least one sensor.
 17. The article of manufacture of claim 16, wherein the data from at least one sensor comprises determined speed of propagation and direction of propagation for the meteorological parameter.
 18. The article of manufacture of claim 16, wherein the computer readable instructions which, when implemented, further cause a computer to carry out a method step comprising predicting an event at a sensor located in a block neighboring an analyzed region based on the physical model.
 19. The article of manufacture of claim 16, wherein the computer readable instructions which, when implemented, further cause a computer to carry out a method step comprising continuously recalibrating the model based on data from at least one sensor located at and around an area neighboring an analyzed region.
 20. A system for forecasting a meteorological parameter, comprising: at least one distinct software module, each distinct software module being embodied on a tangible computer-readable medium; a memory; and at least one processor coupled to the memory and operative for: analyzing geographically distributed sensor network data to assess spatial and temporal variation of a meteorological parameter in real-time, wherein analyzing geographically distributed sensor network data is carried out by a software module executing on the processor; correlating at least two portions of data from the sensor network to identify a temporal and spatial evolution of the meteorological parameter, wherein correlating at least two portions of data from the sensor network to identify a temporal and spatial evolution of the meteorological parameter is carried out by a software module executing on the processor; and forecasting the meteorological parameter based on the temporal and spatial evolution of the meteorological parameter, wherein forecasting the meteorological parameter is carried out by a software module executing on the processor.
 21. The system of claim 20, wherein correlating at least two portions of data comprises correlating data obtained from a near neighbor sensor with data obtained from at least one farther-positioned sensor to determine a pattern that is shifted in time.
 22. The system of claim 20, wherein forecasting the meteorological parameter based on the temporal and spatial evolution of the meteorological parameter comprises training a physical model for a meteorological parameter prediction based on data from at least one sensor.
 23. The system of claim 22, wherein the data from at least one sensor comprises determined speed of propagation and direction of propagation for the meteorological parameter.
 24. The system of claim 22, wherein the at least one processor coupled to the memory is further operative for predicting an event at a sensor located in a block neighboring an analyzed region based on the physical model.
 25. The system of claim 22, wherein the at least one processor coupled to the memory is further operative for continuously recalibrating the model based on data from at least one sensor located at and around an area neighboring an analyzed region. 